Skip to main content

SGLang is yet another fast serving framework for large language models and vision language models.

Project description

logo

PyPI PyPI - Downloads license issue resolution open issues


| Blog | Documentation | Join Slack | Join Bi-Weekly Development Meeting | Slides |

News

  • [2024/10] 🔥 The First SGLang Online Meetup (slides).
  • [2024/09] SGLang v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
  • [2024/07] Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).
More
  • [2024/02] SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
  • [2024/04] SGLang is used by the official LLaVA-NeXT (video) release (blog).
  • [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
  • [2024/01] SGLang powers the serving of the official LLaVA v1.6 release demo (usage).

About

SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. The core features include:

  • Fast Backend Runtime: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
  • Flexible Frontend Language: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
  • Extensive Model Support: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models.
  • Active Community: SGLang is open-source and backed by an active community with industry adoption.

Getting Started

Install SGLang: See https://sgl-project.github.io/start/install.html

Send requests: See https://sgl-project.github.io/start/send_request.html

Backend: SGLang Runtime (SRT)

See https://sgl-project.github.io/backend/backend.html

Frontend: Structured Generation Language (SGLang)

See https://sgl-project.github.io/frontend/frontend.html

Benchmark And Performance

Learn more in our release blogs: v0.2 blog, v0.3 blog

Roadmap

Development Roadmap (2024 Q4)

Citation And Acknowledgment

Please cite our paper, SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful. We also learned from the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sglang-0.3.5.post1.tar.gz (317.6 kB view details)

Uploaded Source

Built Distribution

sglang-0.3.5.post1-py3-none-any.whl (444.0 kB view details)

Uploaded Python 3

File details

Details for the file sglang-0.3.5.post1.tar.gz.

File metadata

  • Download URL: sglang-0.3.5.post1.tar.gz
  • Upload date:
  • Size: 317.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for sglang-0.3.5.post1.tar.gz
Algorithm Hash digest
SHA256 68d6fca02f9f9c3948d8e67e300f632dfe3ef1e0825c06a78903346d894ef445
MD5 b0135c033a79a2403e5fc17265b83ab1
BLAKE2b-256 3916c20fba396f3aa98730325f0a2bed465f446467e33ec723db8c5d576e2a8c

See more details on using hashes here.

File details

Details for the file sglang-0.3.5.post1-py3-none-any.whl.

File metadata

  • Download URL: sglang-0.3.5.post1-py3-none-any.whl
  • Upload date:
  • Size: 444.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for sglang-0.3.5.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 44dbf8794e07837f4b02ef39bbd038eaefa7e27a3ae7d02ca5f9ffd93666b88e
MD5 a4f987276784b0f1b07b51fefecdbb5b
BLAKE2b-256 6b9571da545e3bc5d7b06f1156f95ecc8dfa289a457814c67cf89853da5e4723

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page